abstract = {Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. The paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea. }

+

}

+

</bibtex>

+

+

<bibtex>

+

@article{328110,

+

author = {Anil Jain and Lin Hong and Sharath Pankanti},

+

title = {Biometric identification},

+

journal = {Commun. ACM},

+

volume = {43},

+

number = {2},

+

year = {2000},

+

issn = {0001-0782},

+

pages = {90--98},

+

doi = {http://doi.acm.org/10.1145/328236.328110},

+

publisher = {ACM Press},

+

address = {New York, NY, USA},

+

}

+

+

</bibtex>

+

+

<bibtex>

+

@article{duag002,

+

author = {Daugman, John G. },

+

title = {High Confidence Visual Recognition of Persons by a Test of Statistical Independence},

abstract = {A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte “iris code”. Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical “cross-over” error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about <math>10^31</math> }